POS  Vol.4 No.3 , August 2013
Map Aided Pedestrian Dead Reckoning Using Buildings Information for Indoor Navigation Applications
ABSTRACT
Navigation systems play an important role in many vital disciplines. Determining the location of a user relative to its physical environment is an important part of many indoor-based navigation services such as user navigation, enhanced 911 (E911), law enforcement, location-based and marketing services. Indoor navigation applications require a reliable, trustful and continuous navigation solution that overcomes the challenge of Global Navigation Satellite System (GNSS) signal unavailability. To compensate for this issue, other navigation systems such as Inertial Navigation System (INS) are introduced, however, over time there is a significant amount of drift especially in common with low-cost commercial sensors. In this paper, a map aided navigation solution is developed. This research develops an aiding system that utilizes geospatial data to assist the navigation solution by providing virtual boundaries for the navigation trajectories and limits its possibilities only when it is logical to locate the user on a map. The algorithm develops a Pedestrian Dead Reckoning (PDR) based on smart-phone accelerometer and magnetometer sensors to provide the navigation solution. Geospatial model for two indoor environments with a developed map matching algorithm was used to match and project navigation position estimates on the geospatial map. The developed algorithms were field tested in indoor environments and yielded accurate matching results as well as a significant enhancement to positional accuracy. The achieved results demonstrate that the contribution of the developed map aided system enhances the reliability, usability, and accuracy of navigation trajectories in indoor environments.



Cite this paper
M. Attia, A. Moussa and N. El-Sheimy, "Map Aided Pedestrian Dead Reckoning Using Buildings Information for Indoor Navigation Applications," Positioning, Vol. 4 No. 3, 2013, pp. 227-239. doi: 10.4236/pos.2013.43023.
References
[1]   N. El-Sheimy and X. Niu, “The Promise of MEMS to the Navigation Community,” Inside GNSS, Vol. 2, 2007, pp. 46-56.

[2]   W. Abdel-Hamid, “Accuracy Enhancement of Integrated MEMS-IMU/GPS Systems for Land Vehicular Navigation Applications,” University of Calgary, Calgary, 2004.

[3]   M. Attia, A. Moussa, X, Zhao and N. El-Sheimy, “Assisting Personal Positioning in Indoor Environments Using Map Matching,” Archives of Photogrammetry, Cartography and Remote Sensing, Vol. 22, 2011, pp. 39-49.

[4]   M. A. Quddus, et al., “Current Map-Matching Algorithms for Transport Applications: State of the Art and Future Research Directions,” Transportation Research Part C: Emerging Technologies, Vol. 15, No. 5, 2007, pp. 312-328. doi:10.1016/j.trc.2007.05.002

[5]   C. E. White, et al., “Some Map Matching Algorithms for Personal Navigation Assistants,” Transportation Research Part C: Emerging Technologies, Vol. 8, No. 1-6, 2000, pp. 91-108. doi:10.1016/S0968-090X(00)00026-7

[6]   J. S. Greenfeld, “Matching GPS Observations to Locations on a Digital Map,” Transportation Research Board. Meeting, National Research Council (US), Washington DC, 2002.

[7]   M. Quddus, et al., “A General Map Matching Algorithm for Transport Telematics Applications,” GPS Solutions, Vol. 7, No. 3, 2003, pp. 157-167. doi:10.1007/s10291-003-0069-z

[8]   C. Basnayake, et al., “An HSGPS, Inertial and Map-Matching Integrated Portable Vehicular Navigation System for Uninterrupted Real-Time Vehicular Navigation,” International Journal of Vehicle Information and Communication Systems, Vol. 1, 2005, pp. 131-151. doi:10.1504/IJVICS.2005.007589

[9]   J. B. Bullock, “A Prototype Portable Vehicle Navigation System Utilizing Map Aided GPS,” University of Calgary, Calgary, 1995.

[10]   K. Miesenberger, et al., “A Smart Indoor Navigation Solution Based on Building Information Model and Google Android,” In: Computers Helping People with Special Needs, Vol. 5105, Springer, Berlin/Heidelberg, 2008, pp. 1050-1056.

[11]   P. Arto, et al., “Towards Designing Better Maps for Indoor Navigation: Experiences from a Case Study,” Proceedings of the 8th International Conference on Mobile and Ubiquitous Multimedia, Cambridge, 22-25 November 2009.

[12]   G. Glanzer, et al., “Semi-Autonomous Indoor Positioning Using MEMS-Based Inertial Measurement Units and Building Information,” 6th Workshop on Positioning, Navigation and Communication, Hannover, 19 March 2009, pp. 135-139.

[13]   M. Khider, et al., “The Effect of Maps-Enhanced Novel Movement Models on Pedestrian Navigation Performance,” Proceedings of the 12th Annual European Navigation Conference (ENC 2008), Toulouse, 2008,pp. 22-28.

[14]   X. Zhao, et al., “An Economical and Effective Multi-Sensor Integration for Portable Navigation System,” Proceedings of the 22nd International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS 2009), Savannah, September 2009, pp. 2088-2095.

[15]   S. Shin, et al., “Adaptive Step Length Estimation Algorithm Using Low-Cost MEMS Inertial Sensors,” IEEE Sensors Applications Symposium, San Diego, 6-8 February 2007, pp. 1-5.

[16]   A. Ali, et al., “An Improved Personal Dead-Reckoning Algorithm for Dynamically Changing Smartphone User Modes,” Proceedings of the 25th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS 2012), Nashville, 17-21 September 2012, pp. 2432-2439.

 
 
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